PatchSizePilot
Emanuele Giacomuzzo
2022-08-03
Overview
Data
Meta-ecosystems
All
Equally sized
Biomass
p_boxplots = ds_metaecosystems %>%
mutate(metaecosystem_type = fct_reorder(metaecosystem_type, total_metaecosystem_bioarea)) %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("S_S", "M_M", "L_L")) %>%
ggplot (
aes(
x = day,
y = total_metaecosystem_bioarea,
group = interaction(day, metaecosystem_type),
fill = metaecosystem_type
)
) +
geom_boxplot() +
labs(x = "Day",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = '') +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small-Small",
"Medium-Medium",
"Large-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_x_continuous(breaks = unique(ds_metaecosystems$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(caption = "Vertical grey line: resource flow")
p_boxplots
Beta Diversity
p_boxplots = ds_metaecosystems %>%
mutate(metaecosystem_type = fct_reorder(metaecosystem_type, total_metaecosystem_bioarea)) %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("S_S", "M_M", "L_L")) %>%
ggplot (aes(
x = day,
y = bray_curtis,
group = interaction(day, metaecosystem_type),
fill = metaecosystem_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Beta diversity (Bray-Curtis Index)",
color = '',
fill = '',
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small-Small",
"Medium-Medium",
"Large-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
p_boxplots
Gamma Diversity
p_boxplots = ds_metaecosystems %>%
mutate(metaecosystem_type = fct_reorder(metaecosystem_type, total_metaecosystem_bioarea)) %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("S_S", "M_M", "L_L")) %>%
ggplot (aes(
x = day,
y = gamma_shannon,
group = interaction(day, metaecosystem_type),
fill = metaecosystem_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Gamma diversity (Shannon Index)",
color = '',
fill = '',
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small-Small",
"Medium-Medium",
"Large-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
p_boxplots
MM vs SL
Biomass
ds_metaecosystems %>%
filter (disturbance == disturbance_input,
metaecosystem_type %in% c("S_L", "M_M")) %>%
ggplot (
aes(
x = day,
y = total_metaecosystem_bioarea,
group = system_nr,
fill = system_nr,
color = system_nr,
linetype = metaecosystem_type
)
) +
geom_line () +
labs(
x = "Day",
y = "Meta-ecosystem Total Bioarea (µm²)",
fill = "System nr",
color = "System nr",
linetype = ""
) +
scale_linetype_discrete(labels = c("Medium-Medium",
"Small-Large")) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_x_continuous(breaks = unique(ds_metaecosystems$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(caption = "Vertical grey line: resource flow")
p_boxplots = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("S_L", "M_M")) %>%
ggplot (
aes(
x = day,
y = total_metaecosystem_bioarea,
group = interaction(day, metaecosystem_type),
fill = metaecosystem_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = ''
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_x_continuous(breaks = unique(ds_metaecosystems$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(caption = "Vertical grey line: resource flow")
p_boxplots
filtered_data = ds_metaecosystems %>%
filter(metaecosystem_type %in% c("M_M", "S_L"),
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lmer(
total_metaecosystem_bioarea ~
day +
metaecosystem_type +
metaecosystem_type : day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)
plot(full_model)
qqnorm(resid(full_model))
null_model = lmer(
total_metaecosystem_bioarea ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)
AIC = AIC(full_model, null_model) #When I add baseline_bioarea : day, the AIC of the model stays the same when I take off the ecosystem type.
AIC## df AIC
## full_model 8 1440.690
## null_model 6 1439.892AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Data: filtered_data
## Models:
## null_model: total_metaecosystem_bioarea ~ day + (day | system_nr)
## full_model: total_metaecosystem_bioarea ~ day + metaecosystem_type + metaecosystem_type:day + (day | system_nr)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## null_model 6 1439.9 1452.5 -713.95 1427.9
## full_model 8 1440.7 1457.4 -712.35 1424.7 3.2021 2 0.2017p_value = anova$`Pr(>Chisq)`[2]
p_value = round(p_value, digits = 3)p_boxplots +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value))
Time point 1
Let’s start by looking at whether meta-ecosystem type and disturbance had an effect at time point = 1. At this time point, no disturbance event had yet occurred. Therefore, we would not expect an effect of disturbance. In regards to meta-ecosystem type, there might be an effect if it comes from just the sizes of the two ecosystems.
chosen_time_point = 1
filtered_data = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"),
time_point == chosen_time_point)
day_of_time_point = unique(filtered_data$day)time_point_1 = filtered_data %>%
ggplot (aes(x = metaecosystem_type,
y = total_metaecosystem_bioarea)) +
geom_boxplot() +
labs(
title = paste0(
"Time point = ",
chosen_time_point
),
x = "",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = ''
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
time_point_1
full_model = lm(total_metaecosystem_bioarea ~
metaecosystem_type,
data = filtered_data)
par(mfrow=c(2,3))
plot(full_model, which = 1:5)
null_model = lm(total_metaecosystem_bioarea ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 3 243.5524
## null_model 2 242.3665AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: total_metaecosystem_bioarea ~ metaecosystem_type
## Model 2: total_metaecosystem_bioarea ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 1.2142e+10
## 2 9 1.3172e+10 -1 -1029927000 0.6786 0.4339p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
time_point_1 +
labs(
title = paste0(
"Day = ",
day_of_time_point,
", ΔAIC = ",
deltaAIC ,
", p = ",
p_value,
", Adjusted R2 = ",
R2_P
)
)
Time point 2
chosen_time_point = 2
filtered_data = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"),
time_point == chosen_time_point)
day_of_time_point = unique(filtered_data$day)time_point_2 = filtered_data %>%
ggplot (aes(x = metaecosystem_type,
y = total_metaecosystem_bioarea)) +
geom_boxplot() +
labs(
title = paste0(
"Time point = ",
chosen_time_point
),
x = "",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = ''
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
time_point_2
full_model = lm(total_metaecosystem_bioarea ~
metaecosystem_type,
data = filtered_data)
par(mfrow=c(2,3))
plot(full_model, which = 1:5)
null_model = lm(total_metaecosystem_bioarea ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 3 245.2379
## null_model 2 243.3101AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: total_metaecosystem_bioarea ~ metaecosystem_type
## Model 2: total_metaecosystem_bioarea ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 1.4371e+10
## 2 9 1.4475e+10 -1 -104202831 0.058 0.8157p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
time_point_2 +
labs(
title = paste0(
"Day = ",
day_of_time_point,
", ΔAIC = ",
deltaAIC ,
", p = ",
p_value,
", Adjusted R2 = ",
R2_P
)
)
Time point 3
chosen_time_point = 3
filtered_data = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"),
time_point == chosen_time_point)
day_of_time_point = unique(filtered_data$day)time_point_3 = filtered_data %>%
ggplot (aes(x = metaecosystem_type,
y = total_metaecosystem_bioarea)) +
geom_boxplot() +
labs(
title = paste0(
"Time point = ",
chosen_time_point
),
x = "",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = ''
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
time_point_3
full_model = lm(total_metaecosystem_bioarea ~
metaecosystem_type,
data = filtered_data)
par(mfrow=c(2,3))
plot(full_model, which = 1:5)
null_model = lm(total_metaecosystem_bioarea ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 3 240.6479
## null_model 2 247.4085AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: total_metaecosystem_bioarea ~ metaecosystem_type
## Model 2: total_metaecosystem_bioarea ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 9.0814e+09
## 2 9 2.1808e+10 -1 -1.2727e+10 11.211 0.01011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
time_point_3 +
labs(
title = paste0(
"Day = ",
day_of_time_point,
", ΔAIC = ",
deltaAIC ,
", p = ",
p_value,
", Adjusted R2 = ",
R2_P
)
)
Time point 4
chosen_time_point = 4
filtered_data = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"),
time_point == chosen_time_point)
day_of_time_point = unique(filtered_data$day)time_point_4 = filtered_data %>%
ggplot (aes(x = metaecosystem_type,
y = total_metaecosystem_bioarea)) +
geom_boxplot() +
labs(
title = paste0(
"Time point = ",
chosen_time_point
),
x = "",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = ''
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
time_point_4
full_model = lm(total_metaecosystem_bioarea ~
metaecosystem_type,
data = filtered_data)
par(mfrow=c(2,3))
plot(full_model, which = 1:5)
null_model = lm(total_metaecosystem_bioarea ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 3 248.7535
## null_model 2 247.2519AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: total_metaecosystem_bioarea ~ metaecosystem_type
## Model 2: total_metaecosystem_bioarea ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 2.0426e+10
## 2 9 2.1469e+10 -1 -1043835025 0.4088 0.5404p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
time_point_4 +
labs(
title = paste0(
"Day = ",
day_of_time_point,
", ΔAIC = ",
deltaAIC ,
", p = ",
p_value,
", Adjusted R2 = ",
R2_P
)
)
Time point 5
chosen_time_point = 5
filtered_data = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"),
time_point == chosen_time_point)
day_of_time_point = unique(filtered_data$day)time_point_5 = filtered_data %>%
ggplot (aes(x = metaecosystem_type,
y = total_metaecosystem_bioarea)) +
geom_boxplot() +
labs(
title = paste0(
"Time point = ",
chosen_time_point
),
x = "",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = ''
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
time_point_5
full_model = lm(total_metaecosystem_bioarea ~
metaecosystem_type,
data = filtered_data)
par(mfrow=c(2,3))
plot(full_model, which = 1:5)
null_model = lm(total_metaecosystem_bioarea ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 3 240.9001
## null_model 2 239.1289AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: total_metaecosystem_bioarea ~ metaecosystem_type
## Model 2: total_metaecosystem_bioarea ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 9313404235
## 2 9 9528897119 -1 -215492884 0.1851 0.6784p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
time_point_5 +
labs(
title = paste0(
"Day = ",
day_of_time_point,
", ΔAIC = ",
deltaAIC ,
", p = ",
p_value,
", Adjusted R2 = ",
R2_P
)
)
Time point 6
chosen_time_point = 6
filtered_data = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"),
time_point == chosen_time_point)
day_of_time_point = unique(filtered_data$day)time_point_6 = filtered_data %>%
ggplot (aes(x = metaecosystem_type,
y = total_metaecosystem_bioarea)) +
geom_boxplot() +
labs(
title = paste0(
"Time point = ",
chosen_time_point
),
x = "",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = ''
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
time_point_6
full_model = lm(total_metaecosystem_bioarea ~
metaecosystem_type,
data = filtered_data)
par(mfrow=c(2,3))
plot(full_model, which = 1:5)
null_model = lm(total_metaecosystem_bioarea ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 3 226.1687
## null_model 2 224.6854AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: total_metaecosystem_bioarea ~ metaecosystem_type
## Model 2: total_metaecosystem_bioarea ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 2134661375
## 2 9 2247857851 -1 -113196476 0.4242 0.5331p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
time_point_6 +
labs(
title = paste0(
"Day = ",
day_of_time_point,
", ΔAIC = ",
deltaAIC ,
", p = ",
p_value,
", Adjusted R2 = ",
R2_P
)
)
Time point 7
chosen_time_point = 7
filtered_data = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"),
time_point == chosen_time_point)
day_of_time_point = unique(filtered_data$day)time_point_7 = filtered_data %>%
ggplot (aes(x = metaecosystem_type,
y = total_metaecosystem_bioarea)) +
geom_boxplot() +
labs(
title = paste0(
"Time point = ",
chosen_time_point
),
x = "",
y = "Meta-ecosystem Total Bioarea (µm²)",
color = '',
fill = ''
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
time_point_7
full_model = lm(total_metaecosystem_bioarea ~
metaecosystem_type,
data = filtered_data)
par(mfrow=c(2,3))
plot(full_model, which = 1:5)
null_model = lm(total_metaecosystem_bioarea ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 3 219.4978
## null_model 2 218.0579AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: total_metaecosystem_bioarea ~ metaecosystem_type
## Model 2: total_metaecosystem_bioarea ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 1095508451
## 2 9 1158623185 -1 -63114734 0.4609 0.5164p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
time_point_7 +
labs(
title = paste0(
"Day = ",
day_of_time_point,
", ΔAIC = ",
deltaAIC ,
", p = ",
p_value,
", Adjusted R2 = ",
R2_P
)
)
Beta Diversity
ds_metaecosystems %>%
filter (disturbance == disturbance_input,
metaecosystem_type %in% c("S_L", "M_M")) %>%
ggplot (
aes(
x = day,
y = bray_curtis,
group = system_nr,
fill = system_nr,
color = system_nr,
linetype = metaecosystem_type
)
) +
geom_line () +
labs(
title = paste("Disturbance =", disturbance_input),
x = "Day",
y = "Beta Diversity (Bray-Curtis Index)",
fill = "System nr",
color = "System nr",
linetype = "",
caption = "Vertical grey line: resource flow"
) +
scale_linetype_discrete(labels = c("Medium-Medium",
"Small-Large")) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_x_continuous(breaks = unique(ds_metaecosystems$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_boxplots = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("S_L", "M_M")) %>%
ggplot (aes(
x = day,
y = bray_curtis,
group = interaction(day, metaecosystem_type),
fill = metaecosystem_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Beta diversity (Bray-Curtis Index)",
color = '',
fill = '',
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
p_boxplots
filtered_data = ds_metaecosystems %>%
filter(time_point >= 2,
disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"))full_model = lmer(
bray_curtis ~
day +
metaecosystem_type +
metaecosystem_type : day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)
summary(full_model)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: bray_curtis ~ day + metaecosystem_type + metaecosystem_type:day +
## (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -95.4 -78.7 55.7 -111.4 52
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.31770 -0.80339 -0.00421 0.63375 2.23000
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 1.658e-12 1.288e-06
## day 6.670e-15 8.167e-08 -1.00
## Residual 9.138e-03 9.559e-02
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.053272 0.049188 1.083
## day 0.008414 0.002555 3.293
## metaecosystem_typeS_L -0.029385 0.069563 -0.422
## day:metaecosystem_typeS_L 0.006708 0.003613 1.857
##
## Correlation of Fixed Effects:
## (Intr) day mt_S_L
## day -0.935
## mtcsyst_S_L -0.707 0.661
## dy:mtcs_S_L 0.661 -0.707 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')plot(full_model)
qqnorm(resid(full_model))
null_model = lmer(
bray_curtis ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 8 -95.44362
## null_model 6 -85.84173AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Data: filtered_data
## Models:
## null_model: bray_curtis ~ day + (day | system_nr)
## full_model: bray_curtis ~ day + metaecosystem_type + metaecosystem_type:day + (day | system_nr)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## null_model 6 -85.842 -73.276 48.921 -97.842
## full_model 8 -95.444 -78.689 55.722 -111.444 13.602 2 0.001113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1p_value = anova$`Pr(>Chisq)`[2]
p_value = round(p_value, digits = 3)MM_SL_beta_diversity = p_boxplots +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value))
MM_SL_beta_diversity
Gamma Diversity
ds_metaecosystems %>%
filter (disturbance == disturbance_input,
metaecosystem_type %in% c("S_L", "M_M")) %>%
ggplot (
aes(
x = day,
y = gamma_shannon,
group = system_nr,
fill = system_nr,
color = system_nr,
linetype = metaecosystem_type
)
) +
geom_line () +
labs(
title = paste("Disturbance =", disturbance_input),
x = "Day",
y = "Beta Diversity (Bray-Curtis Index)",
fill = "System nr",
color = "System nr",
linetype = "",
caption = "Vertical grey line: resource flow"
) +
scale_linetype_discrete(labels = c("Medium-Medium",
"Small-Large")) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_x_continuous(breaks = unique(ds_metaecosystems$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_boxplots = ds_metaecosystems %>%
filter(disturbance == disturbance_input,
metaecosystem_type %in% c("S_L", "M_M")) %>%
ggplot (aes(
x = day,
y = gamma_shannon,
group = interaction(day, metaecosystem_type),
fill = metaecosystem_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Gamma diversity (Shannon Index)",
color = '',
fill = '',
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_MM, colour_SL),
labels = c("Medium-Medium",
"Small-Large")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
p_boxplots
filtered_data = ds_metaecosystems %>%
filter(time_point >= 2,
disturbance == disturbance_input,
metaecosystem_type %in% c("M_M", "S_L"))full_model = lmer(
gamma_shannon ~
day +
metaecosystem_type +
metaecosystem_type : day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)
summary(full_model)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: gamma_shannon ~ day + metaecosystem_type + metaecosystem_type:day +
## (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1.8 18.5 7.1 -14.2 52
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2502 -0.5035 0.0383 0.7035 1.7116
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 1.646e-02 0.128291
## day 6.973e-05 0.008351 -1.00
## Residual 4.308e-02 0.207551
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.568064 0.121231 12.935
## day -0.004521 0.006687 -0.676
## metaecosystem_typeS_L 0.116940 0.171447 0.682
## day:metaecosystem_typeS_L -0.001375 0.009457 -0.145
##
## Correlation of Fixed Effects:
## (Intr) day mt_S_L
## day -0.948
## mtcsyst_S_L -0.707 0.670
## dy:mtcs_S_L 0.670 -0.707 -0.948
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')plot(full_model)
qqnorm(resid(full_model))
null_model = lmer(
gamma_shannon ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 8 1.7795503
## null_model 6 0.5805223AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Data: filtered_data
## Models:
## null_model: gamma_shannon ~ day + (day | system_nr)
## full_model: gamma_shannon ~ day + metaecosystem_type + metaecosystem_type:day + (day | system_nr)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## null_model 6 0.58052 13.147 5.7097 -11.419
## full_model 8 1.77955 18.534 7.1102 -14.220 2.801 2 0.2465p_value = anova$`Pr(>Chisq)`[2]
p_value = round(p_value, digits = 3)MM_SL_gamma_diversity = p_boxplots +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value))
MM_SL_gamma_diversity
SL vs SL*
Patches
All
Isolated
Biomass
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = bioarea_per_volume,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Patch Bioarea (µm²/μl)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Alpha Diversity
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = shannon,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Alpha Diversity (Shannon Index)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Species Densities
Ble
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Ble,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Ble Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Cep
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Cep,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Cep Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Col
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Col,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Col Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Eug
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Eug,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Eug Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Eup
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Eup,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Eup Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Lox
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Lox,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Lox Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Pau
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Pau,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Pau Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Pca
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Pca,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Pca Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Spi
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Spi,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Spi Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Spi_te
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Spi_te,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Spi_te Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Tet
ds_patches %>%
filter(disturbance == disturbance_input,
metaecosystem == "no") %>%
mutate(eco_metaeco_type = fct_reorder(eco_metaeco_type, patch_size_volume)) %>%
ggplot(aes(
x = day,
y = Tet,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Tet Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
scale_fill_manual(
values = c(colour_small, colour_medium, colour_large),
labels = c("Small isolated",
"Medium isolated",
"Large isolated")
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
Small
Biomass
ds_patches %>%
filter(disturbance == disturbance_input,
patch_size == "S") %>%
ggplot(
aes(
x = day,
y = bioarea_per_volume,
group = culture_ID,
fill = culture_ID,
color = culture_ID,
linetype = eco_metaeco_type
)
) +
geom_line(stat = "summary", fun = "mean") +
labs(
x = "Day",
y = "Patch Bioarea (µm²/μl)",
linetype = ""
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_linetype_discrete(
labels = c(
"Small isolated",
"Small connected to small",
"Small connected to large"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(caption = "Vertical grey line: resource flow")
ds_patches %>%
filter(patch_size == "S",
disturbance == disturbance_input) %>%
ggplot(
aes(
x = day,
y = bioarea_per_volume,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Patch Bioarea (µm²/μl)",
fill = ""
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(caption = "Vertical grey line: resource flow")
p_effect_size = ds_patches_effect_size %>%
filter(
!time_point == 0,
disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_S)", "S (S_L)")
) %>%
ggplot(aes(x = day,
y = bioarea_per_volume_d,
color = eco_metaeco_type)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = bioarea_per_volume_d_lower,
ymax = bioarea_per_volume_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Bioarea Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(labels = c("Small connected to large",
"Small connnected to small")) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_S)","S (S_L)"))full_model = lm(bioarea_per_volume_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(bioarea_per_volume_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 32.88298
## null_model 2 48.33802AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: bioarea_per_volume_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: bioarea_per_volume_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 4.7301
## 2 11 28.2718 -3 -23.542 13.272 0.001794 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
Alpha Diversity
ds_patches %>%
filter(disturbance == disturbance_input,
patch_size == "S") %>%
ggplot(
aes(
x = day,
y = shannon,
group = culture_ID,
fill = culture_ID,
color = culture_ID,
linetype = eco_metaeco_type
)
) +
geom_line(stat = "summary", fun = "mean") +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
labs(
x = "Day",
y = "Shannon Index",
linetype = "",
caption = "Vertical grey line: resource flow"
) +
scale_linetype_discrete(labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
ds_patches %>%
filter(disturbance == disturbance_input,
patch_size == "S") %>%
ggplot(aes(
x = day,
y = shannon,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
labs(
x = "Day",
y = "Alpha Diversity (Shannon Index)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(
!time_point == 0,
disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_S)", "S (S_L)")
) %>%
ggplot(aes(x = day,
y = shannon_d,
color = eco_metaeco_type)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = shannon_d_lower,
ymax = shannon_d_upper),
width = .2,
position = position_dodge(0.5)
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
) +
labs(
x = "Day",
y = "Shannon Index Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(labels = c("Small connected to large",
"Small connnected to small")) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_S)", "S (S_L)"))full_model = lm(shannon_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(shannon_d ~
1,
data = filtered_data)
anova = anova(full_model, null_model)
AIC = AIC(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: shannon_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: shannon_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 11.232
## 2 11 22.014 -3 -10.782 2.5598 0.128AIC## df AIC
## full_model 5 43.26070
## null_model 2 45.33559p_value = anova$`Pr(>F)`[2]
AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
p_value = round(p_value, digits = 2)
R2_full = round(R2_full, digits = 2)
R2_P = round(R2_P, digits = 2)
deltaAIC = round(deltaAIC, digits = 2)
final_figure = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC, ", p = ", p_value, ", R2 (pach type) = ", R2_P))
final_figure
final_figure +
theme_bw(base_size = presentation_size) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none"
) +
labs(caption = "")
ggsave(here("results", "presentations", "small_alpha.png"))Species Densities
patch_size_input = "S"Ble
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Ble,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Ble Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Ble_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Ble_d_lower,
ymax = Ble_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Ble Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Ble_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Ble_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.54256 -0.20791 -0.03939 0.24344 0.67374
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.699145 0.480613 1.455 0.184
## eco_metaeco_typeS (S_S) -0.527922 0.679689 -0.777 0.460
## eco_metaeco_typeS (S_L):day -0.037243 0.024963 -1.492 0.174
## eco_metaeco_typeS (S_S):day -0.008451 0.024963 -0.339 0.744
##
## Residual standard error: 0.4177 on 8 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.2265, Adjusted R-squared: -0.06363
## F-statistic: 0.7807 on 3 and 8 DF, p-value: 0.5372par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Ble_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 18.23815
## null_model 2 15.31937AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Ble_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Ble_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 1.3959
## 2 11 1.8046 -3 -0.40864 0.7807 0.5372p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Ble_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Ble_small
Cep
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Cep,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Cep Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Cep_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Cep_d_lower,
ymax = Cep_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Cep Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Cep_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Cep_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.69132 -0.31097 -0.06966 0.19031 1.02870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13153 0.65022 -0.202 0.845
## eco_metaeco_typeS (S_S) -0.31176 0.91955 -0.339 0.743
## eco_metaeco_typeS (S_L):day 0.04444 0.03377 1.316 0.225
## eco_metaeco_typeS (S_S):day 0.02348 0.03377 0.695 0.507
##
## Residual standard error: 0.5651 on 8 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.4548, Adjusted R-squared: 0.2503
## F-statistic: 2.224 on 3 and 8 DF, p-value: 0.1628par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Cep_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 25.49221
## null_model 2 26.77084AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Cep_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Cep_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 2.5550
## 2 11 4.6861 -3 -2.1311 2.2243 0.1628p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Cep_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Cep_small
Col
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Col,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Col Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Col_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Col_d_lower,
ymax = Col_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Col Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Col_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Col_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8589 -0.4407 0.1131 0.3124 0.8175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.17736 0.75190 -0.236 0.819
## eco_metaeco_typeS (S_S) -0.01247 1.06335 -0.012 0.991
## eco_metaeco_typeS (S_L):day 0.05244 0.03905 1.343 0.216
## eco_metaeco_typeS (S_S):day 0.01035 0.03905 0.265 0.798
##
## Residual standard error: 0.6535 on 8 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.4302, Adjusted R-squared: 0.2165
## F-statistic: 2.013 on 3 and 8 DF, p-value: 0.1908par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Col_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 28.97914
## null_model 2 29.72785AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Col_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Col_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 3.4165
## 2 11 5.9955 -3 -2.579 2.013 0.1908p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Col_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Col_small
Eug
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Eug,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Eug Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Eug_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Eug_d_lower,
ymax = Eug_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Eug Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 6 row(s) containing missing values (geom_path).
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Eug_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Eug_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## 7 9 13 14 15
## 0.000e+00 -2.776e-17 -3.773e-01 7.545e-01 -3.773e-01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.08953 2.06642 -0.527 0.691
## eco_metaeco_typeS (S_S) 0.64672 2.89790 0.223 0.860
## eco_metaeco_typeS (S_L):day 0.04559 0.16337 0.279 0.827
## eco_metaeco_typeS (S_S):day 0.00383 0.16337 0.023 0.985
##
## Residual standard error: 0.9241 on 1 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.09765, Adjusted R-squared: -2.609
## F-statistic: 0.03607 on 3 and 1 DF, p-value: 0.9866par(mfrow = c(2,3))
plot(full_model, which = 1:5)## Warning: not plotting observations with leverage one:
## 1, 2null_model = lm(Eug_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 15.353164
## null_model 2 9.866942AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Eug_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Eug_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.85402
## 2 4 0.94644 -3 -0.092422 0.0361 0.9866p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Eug_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Eug_small## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 6 row(s) containing missing values (geom_path).
Eup
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Eup,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Eup Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Eup_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Eup_d_lower,
ymax = Eup_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Eup Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Eup_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Eup_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.62632 -0.24352 -0.05568 0.37855 0.50876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.187124 0.506564 0.369 0.721
## eco_metaeco_typeS (S_S) -0.882553 0.716390 -1.232 0.253
## eco_metaeco_typeS (S_L):day 0.003398 0.026311 0.129 0.900
## eco_metaeco_typeS (S_S):day 0.023278 0.026311 0.885 0.402
##
## Residual standard error: 0.4403 on 8 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.3875, Adjusted R-squared: 0.1578
## F-statistic: 1.687 on 3 and 8 DF, p-value: 0.2463par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Eup_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 19.50028
## null_model 2 19.38201AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Eup_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Eup_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 1.5507
## 2 11 2.5316 -3 -0.98091 1.6868 0.2463p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Eup_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Eup_small
Lox
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Lox,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Lox Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Lox_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Lox_d_lower,
ymax = Lox_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Lox Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 3 row(s) containing missing values (geom_path).
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Lox_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Lox_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## 7 8 9 13 14 15 17 18
## -0.27972 0.55944 -0.27972 -0.10389 0.63707 -0.78568 0.32822 -0.07571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.79193 1.39775 -0.567 0.601
## eco_metaeco_typeS (S_S) 0.27675 1.57782 0.175 0.869
## eco_metaeco_typeS (S_L):day 0.06698 0.11239 0.596 0.583
## eco_metaeco_typeS (S_S):day 0.03388 0.03832 0.884 0.427
##
## Residual standard error: 0.6358 on 4 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.2246, Adjusted R-squared: -0.3569
## F-statistic: 0.3863 on 3 and 4 DF, p-value: 0.7697par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Lox_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 19.91137
## null_model 2 15.94665AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Lox_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Lox_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 4 1.6169
## 2 7 2.0853 -3 -0.46841 0.3863 0.7697p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Lox_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Lox_small## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 3 row(s) containing missing values (geom_path).
Pau
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Pau,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Pau Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Pau_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Pau_d_lower,
ymax = Pau_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Pau Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Pau_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Pau_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.51958 -0.08078 0.13608 0.30950 0.61788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.758897 0.790054 -0.961 0.365
## eco_metaeco_typeS (S_S) -0.004108 1.117306 -0.004 0.997
## eco_metaeco_typeS (S_L):day 0.062992 0.041036 1.535 0.163
## eco_metaeco_typeS (S_S):day 0.029295 0.041036 0.714 0.496
##
## Residual standard error: 0.6867 on 8 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.3957, Adjusted R-squared: 0.1691
## F-statistic: 1.746 on 3 and 8 DF, p-value: 0.2349par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Pau_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 30.16710
## null_model 2 30.21141AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Pau_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Pau_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 3.7721
## 2 11 6.2421 -3 -2.47 1.7462 0.2349p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Pau_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Pau_small
Pca
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Pca,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Pca Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Pca_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Pca_d_lower,
ymax = Pca_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Pca Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Pca_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Pca_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18541 -0.18044 0.07589 0.27116 1.08334
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.48406 0.84004 0.576 0.5803
## eco_metaeco_typeS (S_S) 0.60926 1.18799 0.513 0.6219
## eco_metaeco_typeS (S_L):day 0.08174 0.04363 1.873 0.0979 .
## eco_metaeco_typeS (S_S):day -0.05443 0.04363 -1.248 0.2475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7301 on 8 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.7512, Adjusted R-squared: 0.6579
## F-statistic: 8.053 on 3 and 8 DF, p-value: 0.008432par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Pca_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 31.63938
## null_model 2 42.33425AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Pca_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Pca_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 4.2644
## 2 11 17.1423 -3 -12.878 8.0529 0.008432 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Pca_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Pca_small
Spi
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Spi,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Spi Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Spi_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Spi_d_lower,
ymax = Spi_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Spi Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Spi_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Spi_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.38685 -0.15019 -0.01561 0.17460 0.53639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.860297 0.367041 -2.344 0.0471 *
## eco_metaeco_typeS (S_S) 0.141441 0.519075 0.272 0.7921
## eco_metaeco_typeS (S_L):day 0.038288 0.019064 2.008 0.0795 .
## eco_metaeco_typeS (S_S):day 0.005846 0.019064 0.307 0.7669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.319 on 8 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.5531, Adjusted R-squared: 0.3855
## F-statistic: 3.3 on 3 and 8 DF, p-value: 0.07857par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Spi_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 11.76803
## null_model 2 15.43263AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Spi_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Spi_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 0.81413
## 2 11 1.82166 -3 -1.0075 3.3001 0.07857 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Spi_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Spi_small
Spi_te
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Spi_te,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Spi_te Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Spi_te_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Spi_te_d_lower,
ymax = Spi_te_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Spi_te Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 row(s) containing missing values (geom_path).
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Spi_te_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Spi_te_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7785 -0.4592 -0.3043 0.5510 0.8552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09015 0.83646 0.108 0.917
## eco_metaeco_typeS (S_S) 0.17019 1.28491 0.132 0.898
## eco_metaeco_typeS (S_L):day 0.01087 0.04345 0.250 0.810
## eco_metaeco_typeS (S_S):day -0.02368 0.05747 -0.412 0.693
##
## Residual standard error: 0.727 on 7 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.1333, Adjusted R-squared: -0.2382
## F-statistic: 0.3588 on 3 and 7 DF, p-value: 0.7848par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(Spi_te_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 29.23044
## null_model 2 24.80390AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Spi_te_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Spi_te_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 7 3.6997
## 2 10 4.2686 -3 -0.56893 0.3588 0.7848p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Spi_te_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Spi_te_small## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 row(s) containing missing values (geom_path).
Tet
ds_patches %>%
filter(
disturbance == disturbance_input,
patch_size == patch_size_input
) %>%
ggplot(
aes(
x = day,
y = Tet,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)
) +
geom_boxplot() +
labs(
x = "Day",
y = "Tet Density (indiv/μl ?)",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_input,
eco_metaeco_type %in% c("S (S_L)", "S (S_S)"),
day > 0) %>%
ggplot(aes(
x = day,
y = Tet_d,
color = eco_metaeco_type
)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = Tet_d_lower,
ymax = Tet_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Tet Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(
labels = c("Small connected to large",
"Small connnected to small")
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 7 row(s) containing missing values (geom_path).
filtered_data = ds_patches_effect_size %>%
filter(patch_size == patch_size_input,
disturbance == disturbance_input,
time_point >= first_time_point_model,
time_point <= last_time_point_model)full_model = lm(Tet_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = Tet_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## 7 10 11 13
## -4.394e-02 1.758e-01 -1.318e-01 3.643e-17
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.14250 0.35427 -3.225 0.191
## eco_metaeco_typeS (S_S) 0.38370 0.41918 0.915 0.528
## eco_metaeco_typeS (S_L):day 0.07690 0.01903 4.041 0.154
## eco_metaeco_typeS (S_S):day NA NA NA NA
##
## Residual standard error: 0.2241 on 1 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.9675, Adjusted R-squared: 0.9026
## F-statistic: 14.9 on 2 and 1 DF, p-value: 0.1802par(mfrow = c(2,3))
plot(full_model, which = 1:5)## Warning: not plotting observations with leverage one:
## 4null_model = lm(Tet_d ~
1,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 4 1.839698
## null_model 2 11.548924AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: Tet_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: Tet_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0502
## 2 3 1.5460 -2 -1.4958 14.897 0.1802p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
density_Tet_small = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
density_Tet_small## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 7 row(s) containing missing values (geom_path).
Large
Biomass
ds_patches %>%
filter(disturbance == disturbance_input,
patch_size == "L") %>%
ggplot(
aes(
x = day,
y = bioarea_per_volume,
group = culture_ID,
fill = culture_ID,
color = culture_ID,
linetype = eco_metaeco_type
)
) +
geom_line(stat = "summary", fun = "mean") +
labs(
x = "Day",
y = "Patch Bioarea (µm²/μl)",
linetype = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_linetype_discrete(labels = c(
"Large isolated",
"Large connected to large",
"Large connected to small"
)) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
ds_patches %>%
filter(patch_size == "L",
disturbance == disturbance_input) %>%
ggplot(aes(
x = day,
y = bioarea_per_volume,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
labs(
x = "Day",
y = "Patch Bioarea (µm²/μl)",
fill = ""
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_different_size, colour_same_size),
labels = c(
"Small isolated",
"Small connected to large",
"Small connected to small",
caption = "Vertical grey line: resource flow"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) 
p_effect_size = ds_patches_effect_size %>%
filter(
!time_point == 0,
disturbance == disturbance_input,
eco_metaeco_type %in% c("L (L_L)", "L (S_L)")
) %>%
ggplot(aes(x = day,
y = bioarea_per_volume_d,
color = eco_metaeco_type)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = bioarea_per_volume_d_lower,
ymax = bioarea_per_volume_d_upper),
width = .2,
position = position_dodge(0.5)
) +
labs(
x = "Day",
y = "Bioarea Density Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(labels = c("Small connected to large",
"Small connnected to small")) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
disturbance == disturbance_input,
eco_metaeco_type %in% c("L (L_L)", "L (S_L)"))full_model = lm(bioarea_per_volume_d ~
eco_metaeco_type +
day : eco_metaeco_type,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = bioarea_per_volume_d ~ eco_metaeco_type + day:eco_metaeco_type,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.70065 -0.36021 0.05607 0.28222 0.87208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.76759 0.67687 1.134 0.2896
## eco_metaeco_typeL (S_L) -0.17902 0.95723 -0.187 0.8563
## eco_metaeco_typeL (L_L):day -0.07419 0.03516 -2.110 0.0678 .
## eco_metaeco_typeL (S_L):day -0.07262 0.03516 -2.066 0.0727 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5883 on 8 degrees of freedom
## Multiple R-squared: 0.5271, Adjusted R-squared: 0.3497
## F-statistic: 2.972 on 3 and 8 DF, p-value: 0.09688par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(bioarea_per_volume_d ~
day,
data = filtered_data)
AIC = AIC(full_model, null_model)
AIC## df AIC
## full_model 5 26.45599
## null_model 3 22.74915AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
deltaAIC = round(deltaAIC, digits = 2)
anova = anova(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: bioarea_per_volume_d ~ eco_metaeco_type + day:eco_metaeco_type
## Model 2: bioarea_per_volume_d ~ day
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 2.7687
## 2 10 2.8371 -2 -0.068471 0.0989 0.9069p_value = anova$`Pr(>F)`[2]
p_value = round(p_value, digits = 3)
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
R2_P = round(R2_P, digits = 2)
large_biomass = p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC , ", p = ", p_value, ", Adjusted R2 = ", R2_P))
large_biomass
Alpha Diversity
ds_patches %>%
filter(disturbance == disturbance_input,
patch_size == "L") %>%
ggplot(
aes(
x = day,
y = shannon,
group = culture_ID,
fill = culture_ID,
color = culture_ID,
linetype = eco_metaeco_type
)
) +
geom_line(stat = "summary", fun = "mean") +
labs(
x = "Day",
y = "Shannon Index",
linetype = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_linetype_discrete(labels = c(
"Large isolated",
"Large connected to large",
"Large connected to small"
)) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
ds_patches %>%
filter(disturbance == disturbance_input,
patch_size == "L") %>%
ggplot(aes(
x = day,
y = shannon,
group = interaction(day, eco_metaeco_type),
fill = eco_metaeco_type
)) +
geom_boxplot() +
labs(
x = "Day",
y = "Shannon Index",
fill = "",
caption = "Vertical grey line: resource flow"
) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
) +
scale_fill_manual(
values = c(colour_isolated, colour_same_size, colour_different_size),
labels = c(
"Small isolated",
"Large connected to large",
"Large connected to small"
)
) +
scale_x_continuous(breaks = unique(ds_patches$day)) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
)
p_effect_size = ds_patches_effect_size %>%
filter(
!time_point == 0,
disturbance == disturbance_input,
eco_metaeco_type == "L (L_L)" |
eco_metaeco_type == "L (S_L)"
) %>%
ggplot(aes(x = day,
y = shannon_d,
color = eco_metaeco_type)) +
geom_point(position = position_dodge(0.5)) +
geom_line(position = position_dodge(0.5)) +
geom_errorbar(
aes(ymin = shannon_d_lower,
ymax = shannon_d_upper),
width = .2,
position = position_dodge(0.5)
) +
geom_vline(
xintercept = resource_flow_days[1] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[2] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[3] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[4] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[5] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_vline(
xintercept = resource_flow_days[6] + 0.7,
linetype = "dotdash",
color = "grey",
size = 0.7
) +
geom_hline(
yintercept = 0,
linetype = "dotted",
color = "black",
size = 0.7
) +
scale_x_continuous(breaks = unique(ds_patches_effect_size$day)) +
labs(
x = "Day",
y = "Shannon Index Effect Size (Hedge's d)",
color = ""
) +
scale_color_discrete(labels = c("Large connected to large",
"Large connected to small")) +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
p_effect_size
filtered_data = ds_patches_effect_size %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
disturbance == disturbance_input,
eco_metaeco_type %in% c("L (L_L)", "L (S_L)"))full_model = lm(shannon_d ~
eco_metaeco_type +
eco_metaeco_type : day,
data = filtered_data)
summary(full_model)##
## Call:
## lm(formula = shannon_d ~ eco_metaeco_type + eco_metaeco_type:day,
## data = filtered_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.93436 -0.41622 -0.07603 0.52024 0.92419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.21097 0.90758 -1.334 0.219
## eco_metaeco_typeL (S_L) -0.28707 1.28351 -0.224 0.829
## eco_metaeco_typeL (L_L):day 0.04688 0.04714 0.995 0.349
## eco_metaeco_typeL (S_L):day 0.05848 0.04714 1.240 0.250
##
## Residual standard error: 0.7888 on 8 degrees of freedom
## Multiple R-squared: 0.2422, Adjusted R-squared: -0.04192
## F-statistic: 0.8525 on 3 and 8 DF, p-value: 0.5034par(mfrow = c(2,3))
plot(full_model, which = 1:5)
null_model = lm(shannon_d ~
1,
data = filtered_data)
anova = anova(full_model, null_model)
AIC = AIC(full_model, null_model)
anova## Analysis of Variance Table
##
## Model 1: shannon_d ~ eco_metaeco_type + eco_metaeco_type:day
## Model 2: shannon_d ~ 1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 8 4.9778
## 2 11 6.5691 -3 -1.5913 0.8525 0.5034AIC## df AIC
## full_model 5 33.49542
## null_model 2 30.82413p_value = anova$`Pr(>F)`[2]
AIC_full = AIC$AIC[1]
AIC_null = AIC$AIC[2]
deltaAIC = AIC_full - AIC_null
R2_full = glance(full_model)$r.squared
R2_null = glance(null_model)$r.squared
R2_P = R2_full - R2_null
p_value = round(p_value, digits = 2)
R2_full = round(R2_full, digits = 2)
R2_P = round(R2_P, digits = 2)
deltaAIC = round(deltaAIC, digits = 2)
p_effect_size +
labs(title = paste0("ΔAIC = ", deltaAIC, ", p = ", p_value, ", R2 (pach type) = ", R2_P))
Evaporation
Effects of evaporation
We want to know if there was a systematic bias in the evaporation of different treatments (disturbance, patch size) and whether evaporation changed across time. My expectation would be that we would see a difference among the exchanges 2,3 and the exchanges 4,5,6. This is because in exchange 2,3 cultures were microwaved in 15 tubes for 3 minutes and in exchange 4,5,6 cultures were microwaved in 4 tubes for only 1 minute.
Tidy
#Columns: exchange & evaporation
ds_for_evaporation = gather(ds_for_evaporation,
key = exchange,
value = evaporation,
water_add_after_t2:water_add_after_t6)
ds_for_evaporation[ds_for_evaporation == "water_add_after_t2"] = "2"
ds_for_evaporation[ds_for_evaporation == "water_add_after_t3"] = "3"
ds_for_evaporation[ds_for_evaporation == "water_add_after_t4"] = "4"
ds_for_evaporation[ds_for_evaporation == "water_add_after_t5"] = "5"
ds_for_evaporation[ds_for_evaporation == "water_add_after_t6"] = "6"
ds_for_evaporation$evaporation[ds_for_evaporation$exchange == 2] = ds_for_evaporation$evaporation[ds_for_evaporation$exchange == 2] / 2 #This is because exchange contained the topping up of two exchanges
ds_for_evaporation$evaporation[ds_for_evaporation$exchange == 2] = ds_for_evaporation$evaporation[ds_for_evaporation$exchange == 2] + 2 #We need to add 2 ml to the evaporation that happened at the exchange events 1 and 2. This is because we already added 1 ml of water at exchange 1 and 1 ml of water at exchange 2.
#Column: nr_of_tubes_in_rack
ds_for_evaporation$nr_of_tubes_in_rack[ds_for_evaporation$exchange == 1] = 15
ds_for_evaporation$nr_of_tubes_in_rack[ds_for_evaporation$exchange == 2] = 15
ds_for_evaporation$nr_of_tubes_in_rack[ds_for_evaporation$exchange == 3] = 15
ds_for_evaporation$nr_of_tubes_in_rack[ds_for_evaporation$exchange == 4] = 4
ds_for_evaporation$nr_of_tubes_in_rack[ds_for_evaporation$exchange == 5] = 4
ds_for_evaporation$nr_of_tubes_in_rack[ds_for_evaporation$exchange == 6] = 4Plot
ds_for_evaporation %>%
filter(disturbance == disturbance) %>%
ggplot(aes(x = as.character(nr_of_tubes_in_rack),
y = evaporation)) +
geom_boxplot() +
labs(x = "Number of tubes in rack",
y = "Evaporation (ml)")
ds_for_evaporation %>%
filter(disturbance == disturbance) %>%
ggplot(aes(x = as.character(patch_size),
y = evaporation)) +
geom_boxplot() +
labs(x = "Patch size",
y = "Evaporation (ml)")
ds_for_evaporation %>%
filter(disturbance == disturbance) %>%
ggplot(aes(x = as.character(day),
y = evaporation)) +
geom_boxplot() +
labs(x = "Day",
y = "Evaporation (ml)")
ds_for_evaporation %>%
filter(disturbance == disturbance) %>%
ggplot(aes(x = disturbance,
y = evaporation)) +
geom_boxplot() +
labs(x = "Disturbance",
y = "Evaporation (ml)")
It seems like there is no real difference across time, disturbance, or patch type. However, we could also run a mixed effect model to show that they do not.
Mixed effect model
It gives me the following error:
- Error in fn(nM$xeval()) : Downdated VtV is not positive definite
mixed.model = lmer(evaporation ~
patch_size * disturbance * exchange +
(exchange | culture_ID),
data = ds_for_evaporation,
REML = FALSE,
control = lmerControl (optimizer = "Nelder_Mead"))
null.model = lm(evaporation ~
1,
data = ds_for_evaporation)
anova(mixed.model, null.model)Evaporation tests
Evaporation when microwaving 15 falcon tubes at the time
evaporation.test = read.csv(here("data", "evaporation_test","evaporation_test_right.csv"), header = TRUE)
evaporation.test %>%
ggplot(aes (x = as.character(water_pipetted),
y = weight_water_evaporated,
group = interaction(water_pipetted, as.character(rack)),
fill = as.character(rack))) +
geom_boxplot() +
labs(x = "Water volume (ml)" ,
y = "Evaporation (g)",
fill = "Rack replicate")
Evaporation when microwaving 5 tubes with 10 filled or empty tubes
evaporation.test = read.csv(here("data", "evaporation_test", "evaporation_test_fill_nofill.csv"), header = TRUE)
evaporation.test %>%
ggplot(aes (x = all_tubes_water,
y = weight_water_evaporated)) +
geom_boxplot() +
labs(x = "Water in the other 10 tubes" ,
y = "Evaporation (g)",
caption = "When all tubes were filled, they were filled with 6.75 ml of deionised water.")
Video analysis script
rm(list = ls())
setwd("/media/mendel-himself/ID_061_Ema2/PatchSizePilot/training")
# load package
# library(devtools)
# install_github("femoerman/bemovi", ref="master")
library(bemovi)
library(parallel)
library(doParallel)
library(foreach)
#Define memory to be allocated
memory.alloc <- 240000 #-needs_to_be_specified
memory.per.identifier <- 40000 #-needs_to_be_specified
memory.per.linker <- 5000 #-needs_to_be_specified
memory.per.overlay <- 60000 #-needs_to_be_specified
# UNIX
# set paths to tools folder and particle linker
tools.path <-
"/home/mendel-himself/bemovi_tools/" #-needs_to_be_specified
to.particlelinker <- tools.path
# directories and file names
to.data <- paste(getwd(), "/", sep = "")
video.description.folder <- "0_video_description/"
video.description.file <- "video_description.txt"
raw.video.folder <- "1_raw/"
raw.avi.folder <- "1a_raw_avi/"
metadata.folder <- "1b_raw_meta/"
particle.data.folder <- "2_particle_data/"
trajectory.data.folder <- "3_trajectory_data/"
temp.overlay.folder <- "4a_temp_overlays/"
overlay.folder <- "4_overlays/"
merged.data.folder <- "5_merged_data/"
ijmacs.folder <- "ijmacs/"
######################################################################
# VIDEO PARAMETERS
# video frame rate (in frames per second)
fps <- 25 #-needs_to_be_specified
# length of video (in frames)
total_frames <- 125 #-needs_to_be_specified
#Dimensions of the videos in pixels
width = 2048 #-needs_to_be_specified
height = 2048 #-needs_to_be_specified
# measured volume (in microliter) #-needs_to_be_specified
measured_volume <-
34.4 # for Leica M205 C with 1.6 fold magnification, sample height 0.5 mm and Hamamatsu Orca Flash 4
#measured_volume <- 14.9 # for Nikon SMZ1500 with 2 fold magnification, sample height 0.5 mm and Canon 5D Mark III
# size of a pixel (in micrometer) #-needs_to_be_specified
pixel_to_scale <-
4.05 # for Leica M205 C with 1.6 fold magnification, sample height 0.5 mm and Hamamatsu Orca Flash 4
#pixel_to_scale <- 3.79 # for Nikon SMZ1500 with 2 fold magnification, sample height 0.5 mm and Canon 5D Mark III
# specify video file format (one of "avi","cxd","mov","tiff")
# bemovi only works with avi and cxd. other formats are reformated to avi below
video.format <- "cxd" #-needs_to_be_specified
# setup
difference.lag <- 10
thresholds <- c(13, 255) # don't change the second value
# thresholds <- c(50,255)
# MORE PARAMETERS (USUALLY NOT CHANGED)
######################################################################
# FILTERING PARAMETERS
# optimized for Perfex Pro 10 stereomicrocope with Perfex SC38800 (IDS UI-3880LE-M-GL) camera
# tested stereomicroscopes: Perfex Pro 10, Nikon SMZ1500, Leica M205 C
# tested cameras: Perfex SC38800, Canon 5D Mark III, Hamamatsu Orca Flash 4
# tested species: Tet, Col, Pau, Pca, Eug, Chi, Ble, Ceph, Lox, Spi
# min and max size: area in pixels
particle_min_size <- 10
particle_max_size <- 1000
# number of adjacent frames to be considered for linking particles
trajectory_link_range <- 3
# maximum distance a particle can move between two frames
trajectory_displacement <- 16
# these values are in the units defined by the parameters above: fps (seconds), measured_volume (microliters) and pixel_to_scale (micometers)
filter_min_net_disp <- 25
filter_min_duration <- 1
filter_detection_freq <- 0.1
filter_median_step_length <- 3
######################################################################
# VIDEO ANALYSIS
#Check if all tools are installed, and if not install them
check_tools_folder(tools.path)
#Ensure computer has permission to run bftools
system(paste0("chmod a+x ", tools.path, "bftools/bf.sh"))
system(paste0("chmod a+x ", tools.path, "bftools/bfconvert"))
system(paste0("chmod a+x ", tools.path, "bftools/showinf"))
# Convert files to compressed avi (takes approx. 2.25 minutes per video)
convert_to_avi(
to.data,
raw.video.folder,
raw.avi.folder,
metadata.folder,
tools.path,
fps,
video.format
)
# TESTING
# check file format and naming
# check_video_file_names(to.data,raw.avi.folder,video.description.folder,video.description.file)
# check whether the thresholds make sense (set "dark backgroud" and "red")
#check_threshold_values(to.data, raw.avi.folder, ijmacs.folder, 2, difference.lag, thresholds, tools.path, memory.alloc)
# identify particles
locate_and_measure_particles(
to.data,
raw.avi.folder,
particle.data.folder,
difference.lag,
min_size = particle_min_size,
max_size = particle_max_size,
thresholds = thresholds,
tools.path,
memory = memory.alloc,
memory.per.identifier = memory.per.identifier,
max.cores = detectCores() - 1
)
# link the particles
link_particles(
to.data,
particle.data.folder,
trajectory.data.folder,
linkrange = trajectory_link_range,
disp = trajectory_displacement,
start_vid = 1,
memory = memory.alloc,
memory_per_linkerProcess = memory.per.linker,
raw.avi.folder,
max.cores = detectCores() - 1,
max_time = 1
)
# merge info from description file and data
merge_data(
to.data,
particle.data.folder,
trajectory.data.folder,
video.description.folder,
video.description.file,
merged.data.folder
)
# load the merged data
load(paste0(to.data, merged.data.folder, "Master.RData"))
# filter data: minimum net displacement, their duration, the detection frequency and the median step length
trajectory.data.filtered <-
filter_data(
trajectory.data,
filter_min_net_disp,
filter_min_duration,
filter_detection_freq,
filter_median_step_length
)
# summarize trajectory data to individual-based data
morph_mvt <-
summarize_trajectories(
trajectory.data.filtered,
calculate.median = F,
write = T,
to.data,
merged.data.folder
)
# get sample level info
summarize_populations(
trajectory.data.filtered,
morph_mvt,
write = T,
to.data,
merged.data.folder,
video.description.folder,
video.description.file,
total_frames
)
# create overlays for validation
create.subtitle.overlays(
to.data,
traj.data = trajectory.data.filtered,
raw.video.folder,
raw.avi.folder,
temp.overlay.folder,
overlay.folder,
fps,
vid.length = total_frames / fps,
width,
height,
tools.path = tools.path,
overlay.type = "number",
video.format
)
# Create overlays (old method)
create_overlays(
traj.data = trajectory.data.filtered,
to.data = to.data,
merged.data.folder = merged.data.folder,
raw.video.folder = raw.avi.folder,
temp.overlay.folder = "4a_temp_overlays_old/",
overlay.folder = "4_overlays_old/",
width = width,
height = height,
difference.lag = difference.lag,
type = "traj",
predict_spec = F,
contrast.enhancement = 1,
IJ.path = "/home/mendel-himself/bemovi_tools",
memory = memory.alloc,
max.cores = detectCores() - 1,
memory.per.overlay = memory.per.overlay
)
########################################################################
# some cleaning up
#system("rm -r 2_particle_data")
#system("rm -r 3_trajectory_data")
#system("rm -r 4a_temp_overlays")
system("rm -r ijmacs")
########################################################################Here I want to check whether I can distinguish between Tet and Chi (food of Ble). To do so, let’s look at the traits of Ble and Chi.
load(here("data", "morphology", "training.RData"))
training = morph_mvt
rm(morph_mvt)training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = mean_grey)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = mean_area)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = mean_perimeter)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = mean_major)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = mean_minor)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = mean_ar)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = mean_turning)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = duration)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = N_frames)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = max_net)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = net_disp)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = net_speed)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = gross_disp)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = gross_speed)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = max_step)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = min_step)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = max_gross_speed)) +
geom_boxplot()
training %>%
filter(species == "Ble" | species == "Tet") %>%
ggplot(aes(x = species,
y = min_gross_speed)) +
geom_boxplot()
It seems like there’s no great way of telling them apart.
Figures
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## Time difference of 1.357676 mins